Overview

Dataset statistics

Number of variables24
Number of observations433
Missing cells4732
Missing cells (%)45.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory129.9 KiB
Average record size in memory307.2 B

Variable types

Numeric23
Categorical1

Alerts

Municipio has a high cardinality: 73 distinct valuesHigh cardinality
Año is highly overall correlated with Confianza en Guardia Na and 4 other fieldsHigh correlation
Abuso sexual is highly overall correlated with MunicipioHigh correlation
Feminicidio is highly overall correlated with Estado InseguroHigh correlation
Narcomenudeo is highly overall correlated with PoblacionHigh correlation
Poblacion is highly overall correlated with Municipio and 3 other fieldsHigh correlation
Tiene Preocupacion is highly overall correlated with Municipio and 5 other fieldsHigh correlation
Colonia Insegura is highly overall correlated with Municipio and 7 other fieldsHigh correlation
Municipio Inseguro is highly overall correlated with Municipio and 8 other fieldsHigh correlation
Estado Inseguro is highly overall correlated with Feminicidio and 2 other fieldsHigh correlation
Afectacion Forma de Vida is highly overall correlated with Municipio and 7 other fieldsHigh correlation
Confianza en Transito is highly overall correlated with Municipio and 7 other fieldsHigh correlation
Confianza en Policia Mun is highly overall correlated with Municipio and 7 other fieldsHigh correlation
Confianza en Guardia Na is highly overall correlated with Año and 4 other fieldsHigh correlation
Confianza en Ministerio Pub is highly overall correlated with Año and 6 other fieldsHigh correlation
Confianza en Fiscalia G R is highly overall correlated with Año and 4 other fieldsHigh correlation
Confianza en Jueces is highly overall correlated with Municipio and 2 other fieldsHigh correlation
Municipio is highly overall correlated with Abuso sexual and 11 other fieldsHigh correlation
Confianza en Ejercito is highly overall correlated with Año and 2 other fieldsHigh correlation
Homicidio is highly overall correlated with Municipio and 1 other fieldsHigh correlation
Confianza en Policia Est is highly overall correlated with Municipio and 5 other fieldsHigh correlation
Confianza en Policia Min is highly overall correlated with Año and 3 other fieldsHigh correlation
Confianza en Marina is highly overall correlated with Municipio and 5 other fieldsHigh correlation
Tiene Preocupacion has 315 (72.7%) missing valuesMissing
Colonia Insegura has 315 (72.7%) missing valuesMissing
Municipio Inseguro has 315 (72.7%) missing valuesMissing
Estado Inseguro has 315 (72.7%) missing valuesMissing
Afectacion Forma de Vida has 315 (72.7%) missing valuesMissing
Confianza en Transito has 315 (72.7%) missing valuesMissing
Confianza en Policia Mun has 315 (72.7%) missing valuesMissing
Confianza en Policia Est has 315 (72.7%) missing valuesMissing
Confianza en Guardia Na has 315 (72.7%) missing valuesMissing
Confianza en Policia Min has 315 (72.7%) missing valuesMissing
Confianza en Ministerio Pub has 315 (72.7%) missing valuesMissing
Confianza en Fiscalia G R has 315 (72.7%) missing valuesMissing
Confianza en Ejercito has 315 (72.7%) missing valuesMissing
Confianza en Marina has 315 (72.7%) missing valuesMissing
Confianza en Jueces has 315 (72.7%) missing valuesMissing
Municipio is uniformly distributedUniform
Abuso sexual has 245 (56.6%) zerosZeros
Feminicidio has 359 (82.9%) zerosZeros
Homicidio has 139 (32.1%) zerosZeros
Narcomenudeo has 255 (58.9%) zerosZeros
Robo has 125 (28.9%) zerosZeros
Secuestro has 406 (93.8%) zerosZeros
Confianza en Jueces has 5 (1.2%) zerosZeros

Reproduction

Analysis started2022-12-06 06:01:02.999170
Analysis finished2022-12-06 06:02:18.891762
Duration1 minute and 15.89 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

Año
Real number (ℝ)

Distinct6
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.4988
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2022-12-05T23:02:18.964321image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2019
Q32021
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7079942
Coefficient of variation (CV)0.00084575149
Kurtosis-1.2659773
Mean2019.4988
Median Absolute Deviation (MAD)1
Skewness0.0019792995
Sum874443
Variance2.917244
MonotonicityNot monotonic
2022-12-05T23:02:19.255271image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2019 73
16.9%
2017 72
16.6%
2018 72
16.6%
2020 72
16.6%
2021 72
16.6%
2022 72
16.6%
ValueCountFrequency (%)
2017 72
16.6%
2018 72
16.6%
2019 73
16.9%
2020 72
16.6%
2021 72
16.6%
2022 72
16.6%
ValueCountFrequency (%)
2022 72
16.6%
2021 72
16.6%
2020 72
16.6%
2019 73
16.9%
2018 72
16.6%
2017 72
16.6%

Municipio
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM

Distinct73
Distinct (%)16.9%
Missing0
Missing (%)0.0%
Memory size34.3 KiB
Aconchi
 
6
Magdalena
 
6
Sahuaripa
 
6
Rosario
 
6
Rayón
 
6
Other values (68)
403 

Length

Max length29
Median length21
Mean length9.4942263
Min length4

Characters and Unicode

Total characters4111
Distinct characters51
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowAconchi
2nd rowAconchi
3rd rowAconchi
4th rowAconchi
5th rowAconchi

Common Values

ValueCountFrequency (%)
Aconchi 6
 
1.4%
Magdalena 6
 
1.4%
Sahuaripa 6
 
1.4%
Rosario 6
 
1.4%
Rayón 6
 
1.4%
Quiriego 6
 
1.4%
Puerto Peñasco 6
 
1.4%
Pitiquito 6
 
1.4%
Oquitoa 6
 
1.4%
Opodepe 6
 
1.4%
Other values (63) 373
86.1%

Length

2022-12-05T23:02:19.429343image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
san 36
 
5.7%
de 24
 
3.8%
río 12
 
1.9%
la 12
 
1.9%
villa 12
 
1.9%
santa 12
 
1.9%
banámichi 6
 
1.0%
arivechi 6
 
1.0%
arizpe 6
 
1.0%
atil 6
 
1.0%
Other values (84) 499
79.1%

Most occurring characters

ValueCountFrequency (%)
a 621
15.1%
o 282
 
6.9%
e 282
 
6.9%
i 258
 
6.3%
r 246
 
6.0%
198
 
4.8%
c 192
 
4.7%
u 180
 
4.4%
n 169
 
4.1%
s 156
 
3.8%
Other values (41) 1527
37.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3312
80.6%
Uppercase Letter 601
 
14.6%
Space Separator 198
 
4.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 621
18.8%
o 282
 
8.5%
e 282
 
8.5%
i 258
 
7.8%
r 246
 
7.4%
c 192
 
5.8%
u 180
 
5.4%
n 169
 
5.1%
s 156
 
4.7%
l 150
 
4.5%
Other values (18) 776
23.4%
Uppercase Letter
ValueCountFrequency (%)
C 72
12.0%
S 72
12.0%
B 60
10.0%
H 48
 
8.0%
A 48
 
8.0%
P 42
 
7.0%
M 31
 
5.2%
G 30
 
5.0%
N 30
 
5.0%
R 24
 
4.0%
Other values (12) 144
24.0%
Space Separator
ValueCountFrequency (%)
198
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3913
95.2%
Common 198
 
4.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 621
15.9%
o 282
 
7.2%
e 282
 
7.2%
i 258
 
6.6%
r 246
 
6.3%
c 192
 
4.9%
u 180
 
4.6%
n 169
 
4.3%
s 156
 
4.0%
l 150
 
3.8%
Other values (40) 1377
35.2%
Common
ValueCountFrequency (%)
198
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3991
97.1%
None 120
 
2.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 621
15.6%
o 282
 
7.1%
e 282
 
7.1%
i 258
 
6.5%
r 246
 
6.2%
198
 
5.0%
c 192
 
4.8%
u 180
 
4.5%
n 169
 
4.2%
s 156
 
3.9%
Other values (35) 1407
35.3%
None
ValueCountFrequency (%)
á 48
40.0%
í 30
25.0%
é 18
 
15.0%
ó 12
 
10.0%
ú 6
 
5.0%
ñ 6
 
5.0%

Abuso sexual
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct139
Distinct (%)32.2%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.00011571243
Minimum0
Maximum0.002739726
Zeros245
Zeros (%)56.6%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2022-12-05T23:02:19.619584image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.00015347037
95-th percentile0.00048590865
Maximum0.002739726
Range0.002739726
Interquartile range (IQR)0.00015347037

Descriptive statistics

Standard deviation0.00025819477
Coefficient of variation (CV)2.2313486
Kurtosis49.08303
Mean0.00011571243
Median Absolute Deviation (MAD)0
Skewness5.7759502
Sum0.049987769
Variance6.6664538 × 10-8
MonotonicityNot monotonic
2022-12-05T23:02:19.814559image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 245
56.6%
0.000171556013 5
 
1.2%
0.0001169864296 4
 
0.9%
9.320533134 × 10-54
 
0.9%
0.0004859086492 3
 
0.7%
9.011096522 × 10-53
 
0.7%
0.0001053518753 3
 
0.7%
7.977026165 × 10-53
 
0.7%
0.0002021835827 2
 
0.5%
0.0001727787137 2
 
0.5%
Other values (129) 158
36.5%
ValueCountFrequency (%)
0 245
56.6%
1.888345885 × 10-51
 
0.2%
1.912496892 × 10-51
 
0.2%
1.944352628 × 10-51
 
0.2%
2.266015061 × 10-51
 
0.2%
2.549995856 × 10-52
 
0.5%
3.021353415 × 10-51
 
0.2%
3.025810161 × 10-52
 
0.5%
3.262163793 × 10-51
 
0.2%
3.399022592 × 10-51
 
0.2%
ValueCountFrequency (%)
0.002739726027 1
0.2%
0.0027100271 1
0.2%
0.001158748552 1
0.2%
0.001082251082 1
0.2%
0.00102145046 1
0.2%
0.0009082652134 1
0.2%
0.0008976660682 1
0.2%
0.000865950814 2
0.5%
0.0008554319932 1
0.2%
0.000730994152 1
0.2%

Feminicidio
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct54
Distinct (%)12.5%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean1.3578799 × 10-5
Minimum0
Maximum0.0018621974
Zeros359
Zeros (%)82.9%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2022-12-05T23:02:19.994663image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4.1458746 × 10-5
Maximum0.0018621974
Range0.0018621974
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.00010136478
Coefficient of variation (CV)7.4649294
Kurtosis262.97931
Mean1.3578799 × 10-5
Median Absolute Deviation (MAD)0
Skewness15.166902
Sum0.0058660413
Variance1.0274818 × 10-8
MonotonicityNot monotonic
2022-12-05T23:02:20.181118image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 359
82.9%
1.944352628 × 10-54
 
0.9%
1.595176187 × 10-54
 
0.9%
2.244114809 × 10-53
 
0.7%
5.024595394 × 10-62
 
0.5%
1.507378618 × 10-52
 
0.5%
1.888345885 × 10-52
 
0.5%
6.083206093 × 10-62
 
0.5%
6.051620321 × 10-52
 
0.5%
1.287299503 × 10-52
 
0.5%
Other values (44) 50
 
11.5%
ValueCountFrequency (%)
0 359
82.9%
2.136151915 × 10-61
 
0.2%
2.291034723 × 10-61
 
0.2%
3.204227872 × 10-61
 
0.2%
5.024595394 × 10-62
 
0.5%
6.083206093 × 10-62
 
0.5%
6.374989641 × 10-61
 
0.2%
6.408455744 × 10-62
 
0.5%
6.873104169 × 10-61
 
0.2%
7.476531701 × 10-61
 
0.2%
ValueCountFrequency (%)
0.001862197393 1
0.2%
0.0006531678641 1
0.2%
0.0005411255411 1
0.2%
0.0003236245955 1
0.2%
0.0002086375965 1
0.2%
0.0001902225604 1
0.2%
0.000171556013 1
0.2%
0.0001169864296 1
0.2%
0.0001096250822 1
0.2%
0.0001053518753 2
0.5%

Homicidio
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct230
Distinct (%)53.2%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.00050887581
Minimum0
Maximum0.0054794521
Zeros139
Zeros (%)32.1%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2022-12-05T23:02:20.369210image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.00031605563
Q30.00073588305
95-th percentile0.0017429855
Maximum0.0054794521
Range0.0054794521
Interquartile range (IQR)0.00073588305

Descriptive statistics

Standard deviation0.00068535226
Coefficient of variation (CV)1.3467967
Kurtosis14.563562
Mean0.00050887581
Median Absolute Deviation (MAD)0.00031605563
Skewness3.0059853
Sum0.21983435
Variance4.6970772 × 10-7
MonotonicityNot monotonic
2022-12-05T23:02:20.545202image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 139
32.1%
0.001158748552 6
 
1.4%
0.0005411255411 4
 
0.9%
0.0001933114247 4
 
0.9%
0.0006788866259 4
 
0.9%
0.0003586800574 4
 
0.9%
0.000171556013 4
 
0.9%
0.001126126126 3
 
0.7%
0.0001106072337 3
 
0.7%
0.000730994152 3
 
0.7%
Other values (220) 258
59.6%
ValueCountFrequency (%)
0 139
32.1%
4.00384369 × 10-51
 
0.2%
4.609994468 × 10-51
 
0.2%
5.069579985 × 10-51
 
0.2%
5.438980082 × 10-51
 
0.2%
6.959426543 × 10-52
 
0.5%
7.604369978 × 10-51
 
0.2%
8.702368132 × 10-51
 
0.2%
9.320533134 × 10-51
 
0.2%
0.0001013915997 1
 
0.2%
ValueCountFrequency (%)
0.005479452055 1
0.2%
0.005420054201 1
0.2%
0.003755476737 1
0.2%
0.003724394786 1
0.2%
0.003421727973 1
0.2%
0.002656042497 1
0.2%
0.002503651158 2
0.5%
0.002255449048 1
0.2%
0.002205292702 1
0.2%
0.002192982456 1
0.2%

Narcomenudeo
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct154
Distinct (%)35.6%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.0002493488
Minimum0
Maximum0.0048136263
Zeros255
Zeros (%)58.9%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2022-12-05T23:02:20.733051image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.00023397286
95-th percentile0.0012685219
Maximum0.0048136263
Range0.0048136263
Interquartile range (IQR)0.00023397286

Descriptive statistics

Standard deviation0.00055969651
Coefficient of variation (CV)2.2446329
Kurtosis20.644535
Mean0.0002493488
Median Absolute Deviation (MAD)0
Skewness3.9835164
Sum0.10771868
Variance3.1326018 × 10-7
MonotonicityNot monotonic
2022-12-05T23:02:20.926105image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 255
58.9%
0.0001843997787 3
 
0.7%
0.0003901677721 2
 
0.5%
0.0002192501644 2
 
0.5%
2.534789993 × 10-52
 
0.5%
0.000123433932 2
 
0.5%
0.0008203445447 2
 
0.5%
0.0002801316619 2
 
0.5%
0.0005799342741 2
 
0.5%
0.0002201511705 2
 
0.5%
Other values (144) 158
36.5%
ValueCountFrequency (%)
0 255
58.9%
6.083206093 × 10-61
 
0.2%
1.287299503 × 10-51
 
0.2%
1.631081897 × 10-51
 
0.2%
2.433282437 × 10-51
 
0.2%
2.534789993 × 10-52
 
0.5%
3.025810161 × 10-51
 
0.2%
3.190352374 × 10-51
 
0.2%
3.861898509 × 10-52
 
0.5%
4.00384369 × 10-51
 
0.2%
ValueCountFrequency (%)
0.004813626265 1
0.2%
0.00395256917 1
0.2%
0.003476245655 1
0.2%
0.00298963426 1
0.2%
0.0029397904 1
0.2%
0.002625614326 1
0.2%
0.002562543651 1
0.2%
0.002461033634 1
0.2%
0.002099370189 1
0.2%
0.002016129032 1
0.2%

Robo
Real number (ℝ)

Distinct265
Distinct (%)61.3%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.0013631883
Minimum0
Maximum0.023557072
Zeros125
Zeros (%)28.9%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2022-12-05T23:02:21.094094image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.0009525506
Q30.0018624866
95-th percentile0.0042000763
Maximum0.023557072
Range0.023557072
Interquartile range (IQR)0.0018624866

Descriptive statistics

Standard deviation0.001960051
Coefficient of variation (CV)1.4378432
Kurtosis47.012779
Mean0.0013631883
Median Absolute Deviation (MAD)0.0009525506
Skewness5.2871034
Sum0.58889733
Variance3.8417999 × 10-6
MonotonicityNot monotonic
2022-12-05T23:02:21.269141image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 125
28.9%
0.001816530427 4
 
0.9%
0.0006788866259 3
 
0.7%
0.000699790063 3
 
0.7%
0.002739726027 3
 
0.7%
0.0008203445447 3
 
0.7%
0.001371742112 3
 
0.7%
0.001699235344 3
 
0.7%
0.00102145046 2
 
0.5%
0.001317523057 2
 
0.5%
Other values (255) 281
64.9%
ValueCountFrequency (%)
0 125
28.9%
6.959426543 × 10-51
 
0.2%
9.320533134 × 10-51
 
0.2%
0.0001053518753 2
 
0.5%
0.000146767447 1
 
0.2%
0.000171556013 2
 
0.5%
0.0001933114247 2
 
0.5%
0.0002087827963 1
 
0.2%
0.0002534789993 1
 
0.2%
0.000279615994 1
 
0.2%
ValueCountFrequency (%)
0.02355707173 1
0.2%
0.01643835616 1
0.2%
0.01064051742 1
0.2%
0.009193054137 1
0.2%
0.008090614887 1
0.2%
0.006952491309 1
0.2%
0.006356950367 1
0.2%
0.006124291573 1
0.2%
0.005960931918 1
0.2%
0.00595210821 1
0.2%

Secuestro
Real number (ℝ)

Distinct18
Distinct (%)4.2%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean1.1848725 × 10-6
Minimum0
Maximum0.00011698643
Zeros406
Zeros (%)93.8%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2022-12-05T23:02:21.429159image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2.2910347 × 10-6
Maximum0.00011698643
Range0.00011698643
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.818649 × 10-6
Coefficient of variation (CV)7.4426989
Kurtosis104.5615
Mean1.1848725 × 10-6
Median Absolute Deviation (MAD)0
Skewness9.8317313
Sum0.00051186491
Variance7.7768571 × 10-11
MonotonicityNot monotonic
2022-12-05T23:02:21.591915image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 406
93.8%
3.776691769 × 10-64
 
0.9%
2.291034723 × 10-63
 
0.7%
7.338372349 × 10-52
 
0.5%
6.374989641 × 10-62
 
0.5%
1.068075957 × 10-62
 
0.5%
4.582069446 × 10-62
 
0.5%
1.912496892 × 10-51
 
0.2%
3.204227872 × 10-61
 
0.2%
4.609994468 × 10-51
 
0.2%
Other values (8) 8
 
1.8%
ValueCountFrequency (%)
0 406
93.8%
1.068075957 × 10-62
 
0.5%
2.136151915 × 10-61
 
0.2%
2.291034723 × 10-63
 
0.7%
3.204227872 × 10-61
 
0.2%
3.776691769 × 10-64
 
0.9%
4.582069446 × 10-62
 
0.5%
5.024595394 × 10-61
 
0.2%
6.374989641 × 10-62
 
0.5%
7.553383538 × 10-61
 
0.2%
ValueCountFrequency (%)
0.0001169864296 1
0.2%
7.977026165 × 10-51
0.2%
7.338372349 × 10-52
0.5%
4.609994468 × 10-51
0.2%
1.912496892 × 10-51
0.2%
1.507378618 × 10-51
0.2%
1.287299503 × 10-51
0.2%
1.122057404 × 10-51
0.2%
7.553383538 × 10-61
0.2%
6.374989641 × 10-62
0.5%

Poblacion
Real number (ℝ)

Distinct72
Distinct (%)16.7%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean40900.556
Minimum365
Maximum936263
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2022-12-05T23:02:21.772326image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum365
5-th percentile537
Q11184
median3140.5
Q314827.5
95-th percentile199021
Maximum936263
Range935898
Interquartile range (IQR)13643.5

Descriptive statistics

Standard deviation126352.44
Coefficient of variation (CV)3.0892599
Kurtosis34.133192
Mean40900.556
Median Absolute Deviation (MAD)2329.5
Skewness5.5168346
Sum17669040
Variance1.596494 × 1010
MonotonicityNot monotonic
2022-12-05T23:02:21.973320image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2563 6
 
1.4%
33049 6
 
1.4%
5257 6
 
1.4%
4830 6
 
1.4%
1496 6
 
1.4%
3090 6
 
1.4%
62689 6
 
1.4%
9122 6
 
1.4%
496 6
 
1.4%
2438 6
 
1.4%
Other values (62) 372
85.9%
ValueCountFrequency (%)
365 6
1.4%
369 6
1.4%
496 6
1.4%
537 6
1.4%
626 6
1.4%
753 6
1.4%
759 6
1.4%
863 6
1.4%
888 6
1.4%
943 6
1.4%
ValueCountFrequency (%)
936263 6
1.4%
436484 6
1.4%
264782 6
1.4%
199021 6
1.4%
164387 6
1.4%
156863 6
1.4%
91929 6
1.4%
89122 6
1.4%
77682 6
1.4%
62689 6
1.4%

Tiene Preocupacion
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct99
Distinct (%)83.9%
Missing315
Missing (%)72.7%
Infinite0
Infinite (%)0.0%
Mean0.63811889
Minimum0.31578947
Maximum0.92727273
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2022-12-05T23:02:22.191312image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.31578947
5-th percentile0.38680556
Q10.52941176
median0.66666667
Q30.73976608
95-th percentile0.84716667
Maximum0.92727273
Range0.61148325
Interquartile range (IQR)0.21035432

Descriptive statistics

Standard deviation0.14528095
Coefficient of variation (CV)0.22767066
Kurtosis-0.77843547
Mean0.63811889
Median Absolute Deviation (MAD)0.10483696
Skewness-0.30249823
Sum75.298029
Variance0.021106553
MonotonicityNot monotonic
2022-12-05T23:02:22.377416image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6666666667 6
 
1.4%
0.7058823529 2
 
0.5%
0.5 2
 
0.5%
0.53125 2
 
0.5%
0.5625 2
 
0.5%
0.6923076923 2
 
0.5%
0.6315789474 2
 
0.5%
0.45 2
 
0.5%
0.5263157895 2
 
0.5%
0.4444444444 2
 
0.5%
Other values (89) 94
 
21.7%
(Missing) 315
72.7%
ValueCountFrequency (%)
0.3157894737 1
0.2%
0.3333333333 1
0.2%
0.3529411765 1
0.2%
0.3571428571 1
0.2%
0.3684210526 1
0.2%
0.375 1
0.2%
0.3888888889 1
0.2%
0.4 1
0.2%
0.4117647059 1
0.2%
0.4166666667 1
0.2%
ValueCountFrequency (%)
0.9272727273 1
0.2%
0.9 1
0.2%
0.8762886598 1
0.2%
0.8571428571 1
0.2%
0.8525345622 1
0.2%
0.85 1
0.2%
0.8466666667 1
0.2%
0.8461538462 2
0.5%
0.8426966292 1
0.2%
0.8356164384 1
0.2%

Colonia Insegura
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct103
Distinct (%)87.3%
Missing315
Missing (%)72.7%
Infinite0
Infinite (%)0.0%
Mean0.6054455
Minimum0.11111111
Maximum0.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2022-12-05T23:02:22.549448image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.11111111
5-th percentile0.24779412
Q10.5
median0.63392857
Q30.75737101
95-th percentile0.85489691
Maximum0.95
Range0.83888889
Interquartile range (IQR)0.25737101

Descriptive statistics

Standard deviation0.19326907
Coefficient of variation (CV)0.31921795
Kurtosis-0.55838155
Mean0.6054455
Median Absolute Deviation (MAD)0.13392857
Skewness-0.5057011
Sum71.442569
Variance0.037352934
MonotonicityNot monotonic
2022-12-05T23:02:22.768440image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 5
 
1.2%
0.6666666667 3
 
0.7%
0.5555555556 2
 
0.5%
0.4210526316 2
 
0.5%
0.6944444444 2
 
0.5%
0.8421052632 2
 
0.5%
0.6111111111 2
 
0.5%
0.6875 2
 
0.5%
0.6 2
 
0.5%
0.3684210526 2
 
0.5%
Other values (93) 94
 
21.7%
(Missing) 315
72.7%
ValueCountFrequency (%)
0.1111111111 1
0.2%
0.1578947368 1
0.2%
0.2 1
0.2%
0.2142857143 1
0.2%
0.2222222222 1
0.2%
0.2352941176 1
0.2%
0.25 1
0.2%
0.2941176471 1
0.2%
0.2962962963 1
0.2%
0.3 1
0.2%
ValueCountFrequency (%)
0.95 1
0.2%
0.9002320186 1
0.2%
0.8936170213 1
0.2%
0.8923076923 1
0.2%
0.891025641 1
0.2%
0.8659793814 1
0.2%
0.8529411765 1
0.2%
0.8510638298 1
0.2%
0.85 1
0.2%
0.8421052632 2
0.5%

Municipio Inseguro
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct96
Distinct (%)81.4%
Missing315
Missing (%)72.7%
Infinite0
Infinite (%)0.0%
Mean0.76695536
Minimum0.35714286
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2022-12-05T23:02:22.932853image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.35714286
5-th percentile0.49605263
Q10.65416667
median0.80456349
Q30.88873371
95-th percentile0.96811723
Maximum1
Range0.64285714
Interquartile range (IQR)0.23456704

Descriptive statistics

Standard deviation0.15545761
Coefficient of variation (CV)0.20269447
Kurtosis-0.26719959
Mean0.76695536
Median Absolute Deviation (MAD)0.11266497
Skewness-0.71250009
Sum90.500732
Variance0.024167068
MonotonicityNot monotonic
2022-12-05T23:02:23.123656image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6666666667 4
 
0.9%
0.75 3
 
0.7%
0.7222222222 3
 
0.7%
0.95 3
 
0.7%
0.6 3
 
0.7%
0.8181818182 2
 
0.5%
0.5789473684 2
 
0.5%
0.8055555556 2
 
0.5%
0.8421052632 2
 
0.5%
0.8333333333 2
 
0.5%
Other values (86) 92
 
21.2%
(Missing) 315
72.7%
ValueCountFrequency (%)
0.3571428571 1
0.2%
0.375 1
0.2%
0.3888888889 1
0.2%
0.3943661972 1
0.2%
0.4117647059 1
0.2%
0.4736842105 1
0.2%
0.5 1
0.2%
0.5263157895 2
0.5%
0.5294117647 2
0.5%
0.5333333333 1
0.2%
ValueCountFrequency (%)
1 1
 
0.2%
0.9787234043 1
 
0.2%
0.9775280899 1
 
0.2%
0.9721577726 1
 
0.2%
0.9705882353 1
 
0.2%
0.9690721649 1
 
0.2%
0.9679487179 1
 
0.2%
0.95 3
0.7%
0.9473684211 1
 
0.2%
0.9461538462 1
 
0.2%

Estado Inseguro
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct91
Distinct (%)77.1%
Missing315
Missing (%)72.7%
Infinite0
Infinite (%)0.0%
Mean0.66164602
Minimum0.36363636
Maximum0.8988764
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2022-12-05T23:02:23.302714image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.36363636
5-th percentile0.46028583
Q10.57894737
median0.66828479
Q30.75
95-th percentile0.83886986
Maximum0.8988764
Range0.53524004
Interquartile range (IQR)0.17105263

Descriptive statistics

Standard deviation0.1179241
Coefficient of variation (CV)0.17822838
Kurtosis-0.40638169
Mean0.66164602
Median Absolute Deviation (MAD)0.084903616
Skewness-0.2623606
Sum78.074231
Variance0.013906092
MonotonicityNot monotonic
2022-12-05T23:02:23.499351image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7222222222 4
 
0.9%
0.7647058824 4
 
0.9%
0.6666666667 3
 
0.7%
0.625 3
 
0.7%
0.5 3
 
0.7%
0.7368421053 3
 
0.7%
0.8 3
 
0.7%
0.5882352941 2
 
0.5%
0.4615384615 2
 
0.5%
0.7777777778 2
 
0.5%
Other values (81) 89
 
20.6%
(Missing) 315
72.7%
ValueCountFrequency (%)
0.3636363636 1
 
0.2%
0.3962264151 1
 
0.2%
0.4 1
 
0.2%
0.4375 1
 
0.2%
0.4444444444 1
 
0.2%
0.4594594595 1
 
0.2%
0.4604316547 1
 
0.2%
0.4615384615 2
0.5%
0.4705882353 1
 
0.2%
0.5 3
0.7%
ValueCountFrequency (%)
0.8988764045 1
0.2%
0.8947368421 1
0.2%
0.8942307692 1
0.2%
0.8723404255 1
0.2%
0.8684210526 1
0.2%
0.8424657534 1
0.2%
0.8382352941 1
0.2%
0.8333333333 1
0.2%
0.8329466357 1
0.2%
0.8181818182 1
0.2%

Afectacion Forma de Vida
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct99
Distinct (%)83.9%
Missing315
Missing (%)72.7%
Infinite0
Infinite (%)0.0%
Mean0.57189626
Minimum0.071428571
Maximum0.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2022-12-05T23:02:23.682538image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.071428571
5-th percentile0.26693847
Q10.47217195
median0.59611795
Q30.6875
95-th percentile0.83192476
Maximum0.9
Range0.82857143
Interquartile range (IQR)0.21532805

Descriptive statistics

Standard deviation0.17540439
Coefficient of variation (CV)0.30670666
Kurtosis0.0016343442
Mean0.57189626
Median Absolute Deviation (MAD)0.10620119
Skewness-0.51523374
Sum67.483759
Variance0.0307667
MonotonicityNot monotonic
2022-12-05T23:02:23.992200image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6666666667 5
 
1.2%
0.5294117647 4
 
0.9%
0.2777777778 3
 
0.7%
0.5 3
 
0.7%
0.5384615385 2
 
0.5%
0.5789473684 2
 
0.5%
0.3888888889 2
 
0.5%
0.625 2
 
0.5%
0.6875 2
 
0.5%
0.85 2
 
0.5%
Other values (89) 91
 
21.0%
(Missing) 315
72.7%
ValueCountFrequency (%)
0.07142857143 1
 
0.2%
0.1111111111 1
 
0.2%
0.1666666667 1
 
0.2%
0.1875 1
 
0.2%
0.2222222222 1
 
0.2%
0.2631578947 1
 
0.2%
0.2676056338 1
 
0.2%
0.2777777778 3
0.7%
0.3058823529 1
 
0.2%
0.3125 1
 
0.2%
ValueCountFrequency (%)
0.9 1
0.2%
0.8823529412 1
0.2%
0.8723404255 1
0.2%
0.85 2
0.5%
0.8421052632 1
0.2%
0.8301282051 1
0.2%
0.8259860789 1
0.2%
0.8235294118 1
0.2%
0.8 2
0.5%
0.7962962963 1
0.2%

Confianza en Transito
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct97
Distinct (%)82.2%
Missing315
Missing (%)72.7%
Infinite0
Infinite (%)0.0%
Mean0.34351056
Minimum0.125
Maximum0.77777778
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2022-12-05T23:02:24.196949image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.125
5-th percentile0.175
Q10.2739899
median0.33333333
Q30.39349376
95-th percentile0.52678019
Maximum0.77777778
Range0.65277778
Interquartile range (IQR)0.11950386

Descriptive statistics

Standard deviation0.1110004
Coefficient of variation (CV)0.32313534
Kurtosis1.5248997
Mean0.34351056
Median Absolute Deviation (MAD)0.060606061
Skewness0.7452114
Sum40.534246
Variance0.012321089
MonotonicityNot monotonic
2022-12-05T23:02:24.380318image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 3
 
0.7%
0.3888888889 3
 
0.7%
0.3333333333 3
 
0.7%
0.2631578947 3
 
0.7%
0.35 2
 
0.5%
0.4375 2
 
0.5%
0.5263157895 2
 
0.5%
0.25 2
 
0.5%
0.2 2
 
0.5%
0.2777777778 2
 
0.5%
Other values (87) 94
 
21.7%
(Missing) 315
72.7%
ValueCountFrequency (%)
0.125 2
0.5%
0.1538461538 1
0.2%
0.1578947368 1
0.2%
0.1666666667 2
0.5%
0.1764705882 1
0.2%
0.1875 2
0.5%
0.1904761905 1
0.2%
0.1914893617 1
0.2%
0.2 2
0.5%
0.2105263158 1
0.2%
ValueCountFrequency (%)
0.7777777778 1
 
0.2%
0.6666666667 1
 
0.2%
0.6111111111 1
 
0.2%
0.55 1
 
0.2%
0.5352112676 1
 
0.2%
0.5294117647 1
 
0.2%
0.5263157895 2
0.5%
0.5090909091 1
 
0.2%
0.5 3
0.7%
0.4864864865 1
 
0.2%

Confianza en Policia Mun
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct98
Distinct (%)83.1%
Missing315
Missing (%)72.7%
Infinite0
Infinite (%)0.0%
Mean0.43854757
Minimum0.055555556
Maximum0.88235294
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2022-12-05T23:02:24.583956image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.055555556
5-th percentile0.26315789
Q10.35153317
median0.41845577
Q30.5
95-th percentile0.68657895
Maximum0.88235294
Range0.82679739
Interquartile range (IQR)0.14846683

Descriptive statistics

Standard deviation0.13327902
Coefficient of variation (CV)0.30391007
Kurtosis1.2087277
Mean0.43854757
Median Absolute Deviation (MAD)0.074446196
Skewness0.72184189
Sum51.748614
Variance0.017763298
MonotonicityNot monotonic
2022-12-05T23:02:24.766393image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 5
 
1.2%
0.3333333333 3
 
0.7%
0.3888888889 3
 
0.7%
0.6470588235 2
 
0.5%
0.4042553191 2
 
0.5%
0.4736842105 2
 
0.5%
0.375 2
 
0.5%
0.2631578947 2
 
0.5%
0.6842105263 2
 
0.5%
0.3 2
 
0.5%
Other values (88) 93
 
21.5%
(Missing) 315
72.7%
ValueCountFrequency (%)
0.05555555556 1
0.2%
0.1666666667 1
0.2%
0.2363636364 1
0.2%
0.25 1
0.2%
0.2556390977 1
0.2%
0.2631578947 2
0.5%
0.2777777778 1
0.2%
0.2972972973 1
0.2%
0.2978723404 1
0.2%
0.2989690722 1
0.2%
ValueCountFrequency (%)
0.8823529412 1
0.2%
0.8333333333 1
0.2%
0.7777777778 1
0.2%
0.7058823529 1
0.2%
0.7 2
0.5%
0.6842105263 2
0.5%
0.6666666667 1
0.2%
0.6470588235 2
0.5%
0.6413043478 1
0.2%
0.625 2
0.5%

Confianza en Policia Est
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct94
Distinct (%)79.7%
Missing315
Missing (%)72.7%
Infinite0
Infinite (%)0.0%
Mean0.47558665
Minimum0.22222222
Maximum0.77777778
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2022-12-05T23:02:24.921351image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.22222222
5-th percentile0.29053926
Q10.40433389
median0.47578016
Q30.54786379
95-th percentile0.6525
Maximum0.77777778
Range0.55555556
Interquartile range (IQR)0.1435299

Descriptive statistics

Standard deviation0.11400765
Coefficient of variation (CV)0.23972003
Kurtosis-0.23741147
Mean0.47558665
Median Absolute Deviation (MAD)0.072964224
Skewness0.034528556
Sum56.119224
Variance0.012997743
MonotonicityNot monotonic
2022-12-05T23:02:25.084978image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 7
 
1.6%
0.4736842105 4
 
0.9%
0.4444444444 3
 
0.7%
0.4375 3
 
0.7%
0.6666666667 2
 
0.5%
0.6315789474 2
 
0.5%
0.5263157895 2
 
0.5%
0.5384615385 2
 
0.5%
0.6 2
 
0.5%
0.25 2
 
0.5%
Other values (84) 89
 
20.6%
(Missing) 315
72.7%
ValueCountFrequency (%)
0.2222222222 2
0.5%
0.25 2
0.5%
0.2727272727 1
0.2%
0.2857142857 1
0.2%
0.2913907285 1
0.2%
0.2941176471 1
0.2%
0.2972972973 1
0.2%
0.3055555556 1
0.2%
0.3058823529 1
0.2%
0.3125 1
0.2%
ValueCountFrequency (%)
0.7777777778 1
0.2%
0.7333333333 1
0.2%
0.6875 1
0.2%
0.6842105263 1
0.2%
0.6666666667 2
0.5%
0.65 1
0.2%
0.6478873239 1
0.2%
0.6470588235 2
0.5%
0.6428571429 1
0.2%
0.6315789474 2
0.5%

Confianza en Guardia Na
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct99
Distinct (%)83.9%
Missing315
Missing (%)72.7%
Infinite0
Infinite (%)0.0%
Mean0.49978162
Minimum0.15384615
Maximum0.89473684
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2022-12-05T23:02:25.289273image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.15384615
5-th percentile0.20982972
Q10.38512821
median0.5172619
Q30.61111111
95-th percentile0.73727469
Maximum0.89473684
Range0.74089069
Interquartile range (IQR)0.22598291

Descriptive statistics

Standard deviation0.16774458
Coefficient of variation (CV)0.33563575
Kurtosis-0.48754359
Mean0.49978162
Median Absolute Deviation (MAD)0.10990712
Skewness-0.23849675
Sum58.974232
Variance0.028138244
MonotonicityNot monotonic
2022-12-05T23:02:25.462937image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 6
 
1.4%
0.6111111111 4
 
0.9%
0.5294117647 3
 
0.7%
0.25 3
 
0.7%
0.1666666667 2
 
0.5%
0.7368421053 2
 
0.5%
0.5263157895 2
 
0.5%
0.6666666667 2
 
0.5%
0.5882352941 2
 
0.5%
0.4577464789 2
 
0.5%
Other values (89) 90
 
20.8%
(Missing) 315
72.7%
ValueCountFrequency (%)
0.1538461538 1
 
0.2%
0.15625 1
 
0.2%
0.1666666667 2
0.5%
0.202247191 1
 
0.2%
0.2058823529 1
 
0.2%
0.2105263158 1
 
0.2%
0.2222222222 1
 
0.2%
0.222737819 1
 
0.2%
0.2352941176 2
0.5%
0.25 3
0.7%
ValueCountFrequency (%)
0.8947368421 1
0.2%
0.8732394366 1
0.2%
0.8 1
0.2%
0.7894736842 1
0.2%
0.7777777778 1
0.2%
0.7397260274 1
0.2%
0.7368421053 2
0.5%
0.7281553398 1
0.2%
0.7034482759 1
0.2%
0.7 1
0.2%

Confianza en Policia Min
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct100
Distinct (%)84.7%
Missing315
Missing (%)72.7%
Infinite0
Infinite (%)0.0%
Mean0.28631911
Minimum0
Maximum0.82442748
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2022-12-05T23:02:25.680370image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.098636364
Q10.1769958
median0.2544075
Q30.35138889
95-th percentile0.62065058
Maximum0.82442748
Range0.82442748
Interquartile range (IQR)0.17439309

Descriptive statistics

Standard deviation0.1549802
Coefficient of variation (CV)0.54128484
Kurtosis1.150288
Mean0.28631911
Median Absolute Deviation (MAD)0.086477987
Skewness1.0615802
Sum33.785655
Variance0.024018861
MonotonicityNot monotonic
2022-12-05T23:02:25.855441image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2222222222 3
 
0.7%
0.3888888889 3
 
0.7%
0.3125 3
 
0.7%
0.3157894737 3
 
0.7%
0.3 2
 
0.5%
0.5 2
 
0.5%
0.3684210526 2
 
0.5%
0.1764705882 2
 
0.5%
0.1176470588 2
 
0.5%
0.2777777778 2
 
0.5%
Other values (90) 94
 
21.7%
(Missing) 315
72.7%
ValueCountFrequency (%)
0 1
0.2%
0.05263157895 2
0.5%
0.05555555556 1
0.2%
0.0625 1
0.2%
0.09090909091 1
0.2%
0.1 1
0.2%
0.1081081081 1
0.2%
0.1111111111 1
0.2%
0.1176470588 2
0.5%
0.1212121212 1
0.2%
ValueCountFrequency (%)
0.8244274809 1
0.2%
0.7058823529 1
0.2%
0.6666666667 1
0.2%
0.6527777778 1
0.2%
0.6333333333 1
0.2%
0.625 1
0.2%
0.6198830409 1
0.2%
0.6148491879 1
0.2%
0.606741573 1
0.2%
0.5638297872 1
0.2%

Confianza en Ministerio Pub
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct94
Distinct (%)79.7%
Missing315
Missing (%)72.7%
Infinite0
Infinite (%)0.0%
Mean0.32125379
Minimum0.052631579
Maximum0.82758621
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2022-12-05T23:02:26.021153image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.052631579
5-th percentile0.12067736
Q10.21997863
median0.29455399
Q30.38782051
95-th percentile0.60076253
Maximum0.82758621
Range0.77495463
Interquartile range (IQR)0.16784188

Descriptive statistics

Standard deviation0.14907545
Coefficient of variation (CV)0.46404262
Kurtosis1.2432011
Mean0.32125379
Median Absolute Deviation (MAD)0.084027675
Skewness0.99221395
Sum37.907947
Variance0.02222349
MonotonicityNot monotonic
2022-12-05T23:02:26.225431image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3333333333 5
 
1.2%
0.25 4
 
0.9%
0.2105263158 4
 
0.9%
0.2 3
 
0.7%
0.2857142857 3
 
0.7%
0.5 3
 
0.7%
0.2777777778 3
 
0.7%
0.1875 3
 
0.7%
0.4 2
 
0.5%
0.3125 2
 
0.5%
Other values (84) 86
 
19.9%
(Missing) 315
72.7%
ValueCountFrequency (%)
0.05263157895 1
0.2%
0.05882352941 1
0.2%
0.06666666667 1
0.2%
0.1063829787 1
0.2%
0.1081081081 1
0.2%
0.1176470588 1
0.2%
0.1212121212 1
0.2%
0.1355932203 1
0.2%
0.1366906475 1
0.2%
0.1470588235 1
0.2%
ValueCountFrequency (%)
0.8275862069 1
0.2%
0.7857142857 1
0.2%
0.7 1
0.2%
0.6842105263 1
0.2%
0.6666666667 1
0.2%
0.6470588235 1
0.2%
0.5925925926 1
0.2%
0.5789473684 1
0.2%
0.5760368664 1
0.2%
0.5625 1
0.2%

Confianza en Fiscalia G R
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct99
Distinct (%)83.9%
Missing315
Missing (%)72.7%
Infinite0
Infinite (%)0.0%
Mean0.25308762
Minimum0
Maximum0.6
Zeros2
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2022-12-05T23:02:26.406444image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.061948529
Q10.16666667
median0.23861284
Q30.33111954
95-th percentile0.47621648
Maximum0.6
Range0.6
Interquartile range (IQR)0.16445288

Descriptive statistics

Standard deviation0.12529644
Coefficient of variation (CV)0.49507138
Kurtosis-0.1045068
Mean0.25308762
Median Absolute Deviation (MAD)0.079430876
Skewness0.34253519
Sum29.864339
Variance0.015699197
MonotonicityNot monotonic
2022-12-05T23:02:26.567236image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1666666667 5
 
1.2%
0.0625 3
 
0.7%
0.3125 2
 
0.5%
0.2127659574 2
 
0.5%
0.3157894737 2
 
0.5%
0.2777777778 2
 
0.5%
0.1052631579 2
 
0.5%
0.2 2
 
0.5%
0.4210526316 2
 
0.5%
0.05882352941 2
 
0.5%
Other values (89) 94
 
21.7%
(Missing) 315
72.7%
ValueCountFrequency (%)
0 2
0.5%
0.03846153846 1
 
0.2%
0.05405405405 1
 
0.2%
0.05882352941 2
0.5%
0.0625 3
0.7%
0.07407407407 1
 
0.2%
0.08510638298 1
 
0.2%
0.09090909091 1
 
0.2%
0.09375 1
 
0.2%
0.1 1
 
0.2%
ValueCountFrequency (%)
0.6 1
0.2%
0.5675675676 1
0.2%
0.5333333333 1
0.2%
0.5294117647 1
0.2%
0.5 1
0.2%
0.4905660377 1
0.2%
0.4736842105 1
0.2%
0.4444444444 2
0.5%
0.4382402707 1
0.2%
0.4210526316 2
0.5%

Confianza en Ejercito
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct96
Distinct (%)81.4%
Missing315
Missing (%)72.7%
Infinite0
Infinite (%)0.0%
Mean0.67539363
Minimum0.026490066
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2022-12-05T23:02:26.777048image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.026490066
5-th percentile0.1103268
Q10.69125874
median0.76370887
Q30.81540583
95-th percentile0.92672065
Maximum1
Range0.97350993
Interquartile range (IQR)0.12414709

Descriptive statistics

Standard deviation0.25329287
Coefficient of variation (CV)0.37503
Kurtosis0.8167922
Mean0.67539363
Median Absolute Deviation (MAD)0.065952463
Skewness-1.459522
Sum79.696448
Variance0.064157278
MonotonicityNot monotonic
2022-12-05T23:02:26.961705image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7894736842 5
 
1.2%
0.7222222222 5
 
1.2%
0.8125 3
 
0.7%
0.8823529412 3
 
0.7%
0.8421052632 3
 
0.7%
0.1666666667 2
 
0.5%
0.1333333333 2
 
0.5%
0.75 2
 
0.5%
0.7647058824 2
 
0.5%
0.6923076923 2
 
0.5%
Other values (86) 89
 
20.6%
(Missing) 315
72.7%
ValueCountFrequency (%)
0.02649006623 1
0.2%
0.05555555556 1
0.2%
0.0625 1
0.2%
0.07692307692 1
0.2%
0.1052631579 1
0.2%
0.1058823529 1
0.2%
0.1111111111 1
0.2%
0.1333333333 2
0.5%
0.1428571429 1
0.2%
0.1489361702 1
0.2%
ValueCountFrequency (%)
1 1
 
0.2%
0.972972973 1
 
0.2%
0.9523809524 1
 
0.2%
0.95 2
0.5%
0.9473684211 1
 
0.2%
0.9230769231 1
 
0.2%
0.8888888889 1
 
0.2%
0.8854961832 1
 
0.2%
0.8850574713 1
 
0.2%
0.8823529412 3
0.7%

Confianza en Marina
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct101
Distinct (%)85.6%
Missing315
Missing (%)72.7%
Infinite0
Infinite (%)0.0%
Mean0.65135388
Minimum0.21428571
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2022-12-05T23:02:27.139678image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.21428571
5-th percentile0.35651261
Q10.5625
median0.66326531
Q30.75541677
95-th percentile0.87589869
Maximum1
Range0.78571429
Interquartile range (IQR)0.19291677

Descriptive statistics

Standard deviation0.15033018
Coefficient of variation (CV)0.23079647
Kurtosis0.17893857
Mean0.65135388
Median Absolute Deviation (MAD)0.10076531
Skewness-0.47272508
Sum76.859758
Variance0.022599162
MonotonicityNot monotonic
2022-12-05T23:02:27.340875image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 4
 
0.9%
0.7894736842 3
 
0.7%
0.5625 3
 
0.7%
0.5555555556 3
 
0.7%
0.8 3
 
0.7%
0.5106382979 2
 
0.5%
0.8333333333 2
 
0.5%
0.6666666667 2
 
0.5%
0.6 2
 
0.5%
0.6111111111 2
 
0.5%
Other values (91) 92
 
21.2%
(Missing) 315
72.7%
ValueCountFrequency (%)
0.2142857143 1
0.2%
0.2941176471 1
0.2%
0.3125 1
0.2%
0.3157894737 1
0.2%
0.3333333333 1
0.2%
0.3529411765 1
0.2%
0.3571428571 1
0.2%
0.3636363636 1
0.2%
0.3888888889 1
0.2%
0.4074074074 1
0.2%
ValueCountFrequency (%)
1 1
0.2%
0.9411764706 1
0.2%
0.9375 1
0.2%
0.8888888889 1
0.2%
0.8823529412 1
0.2%
0.8761061947 1
0.2%
0.875862069 1
0.2%
0.8571428571 1
0.2%
0.85 1
0.2%
0.8421052632 1
0.2%

Confianza en Jueces
Real number (ℝ)

HIGH CORRELATION
MISSING
ZEROS

Distinct96
Distinct (%)81.4%
Missing315
Missing (%)72.7%
Infinite0
Infinite (%)0.0%
Mean0.26845168
Minimum0
Maximum0.94444444
Zeros5
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2022-12-05T23:02:27.532053image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.033359014
Q10.13149249
median0.24573291
Q30.35077766
95-th percentile0.6861624
Maximum0.94444444
Range0.94444444
Interquartile range (IQR)0.21928517

Descriptive statistics

Standard deviation0.18446576
Coefficient of variation (CV)0.68714697
Kurtosis1.3264273
Mean0.26845168
Median Absolute Deviation (MAD)0.11239496
Skewness1.0829122
Sum31.677298
Variance0.034027616
MonotonicityNot monotonic
2022-12-05T23:02:27.708036image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5
 
1.2%
0.2631578947 4
 
0.9%
0.25 3
 
0.7%
0.1666666667 3
 
0.7%
0.3333333333 3
 
0.7%
0.375 2
 
0.5%
0.2222222222 2
 
0.5%
0.1052631579 2
 
0.5%
0.5 2
 
0.5%
0.1 2
 
0.5%
Other values (86) 90
 
20.8%
(Missing) 315
72.7%
ValueCountFrequency (%)
0 5
1.2%
0.0303030303 1
 
0.2%
0.03389830508 1
 
0.2%
0.05 1
 
0.2%
0.05405405405 1
 
0.2%
0.05769230769 1
 
0.2%
0.05882352941 1
 
0.2%
0.06060606061 1
 
0.2%
0.0625 1
 
0.2%
0.06666666667 1
 
0.2%
ValueCountFrequency (%)
0.9444444444 1
0.2%
0.7555555556 1
0.2%
0.7142857143 1
0.2%
0.7105263158 1
0.2%
0.7058823529 1
0.2%
0.7034482759 1
0.2%
0.6831119545 1
0.2%
0.6820276498 1
0.2%
0.5531914894 1
0.2%
0.5352112676 1
0.2%

Interactions

2022-12-05T23:02:14.064009image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:06.648240image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:08.985561image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:11.248679image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:14.476613image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:17.739187image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:20.338795image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:23.830392image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:27.205482image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:30.564470image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:33.990588image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:37.205325image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:40.509170image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:43.583849image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:46.258778image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:48.989208image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:51.617082image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:54.517741image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:57.772831image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:02:00.833604image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:02:04.523091image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:02:08.079485image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:02:10.760123image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:02:14.206780image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:06.783949image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:09.085540image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:11.339405image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:14.637496image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:17.920202image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:20.448891image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:23.965728image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:27.305540image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:30.703475image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:34.103029image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:37.279194image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2022-12-05T23:02:10.367546image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:02:13.306232image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:02:16.634548image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:08.658755image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:10.868526image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:13.868499image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:17.096765image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:19.987423image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:23.189521image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:26.667409image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:29.966511image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:33.389696image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:36.549266image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:39.880713image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:43.106287image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:45.679511image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:48.424933image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:51.111145image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:54.042020image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:57.329654image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:02:00.216457image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:02:03.921342image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:02:07.493758image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:02:10.443036image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:02:13.498986image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:02:16.791596image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:08.741285image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:10.970793image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:13.994452image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:17.283097image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:20.073426image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:23.351293image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:26.757538image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:30.087008image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:33.561996image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:36.701248image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:40.040698image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:43.234623image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:45.788445image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:48.581081image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:51.213729image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:54.175135image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:57.466085image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:02:00.382382image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:02:04.071739image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:02:07.652401image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:02:10.511087image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:02:13.616409image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:02:16.921395image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:08.826982image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:11.071445image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:14.142383image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:17.440648image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:20.167137image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:23.489999image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:26.866225image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:30.218000image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:33.705507image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:36.885221image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:40.201700image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:43.353684image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:45.891457image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:48.716302image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:51.346518image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:54.287741image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:57.582856image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:02:00.532372image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:02:04.220113image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:02:07.832409image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:02:10.592879image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:02:13.729050image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:02:17.047969image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:08.911636image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:11.169282image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:14.315441image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:17.587603image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:20.257942image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:23.637997image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:26.982044image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:30.415980image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:33.855462image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:36.976328image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:40.389338image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:43.479173image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:46.150443image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:48.869930image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:51.465479image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:54.410781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:01:57.686461image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:02:00.721607image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:02:04.393093image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:02:07.986487image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:02:10.672832image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T23:02:13.886968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2022-12-05T23:02:27.862937image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-12-05T23:02:28.222029image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-05T23:02:28.688026image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-05T23:02:29.035760image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-05T23:02:29.393427image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-05T23:02:17.316365image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-05T23:02:17.813802image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-12-05T23:02:18.396310image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

AñoMunicipioAbuso sexualFeminicidioHomicidioNarcomenudeoRoboSecuestroPoblacionTiene PreocupacionColonia InseguraMunicipio InseguroEstado InseguroAfectacion Forma de VidaConfianza en TransitoConfianza en Policia MunConfianza en Policia EstConfianza en Guardia NaConfianza en Policia MinConfianza en Ministerio PubConfianza en Fiscalia G RConfianza en EjercitoConfianza en MarinaConfianza en Jueces
02017Aconchi0.0000000.0000000.0007800.0003900.0027310.02563.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
12018Aconchi0.0000000.0000000.0000000.0003900.0019510.02563.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
22019Aconchi0.0000000.0000000.0000000.0000000.0007800.02563.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
32020Aconchi0.0000000.0000000.0003900.0000000.0000000.02563.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
42021Aconchi0.0003900.0000000.0000000.0000000.0015610.02563.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
52022Aconchi0.0000000.0000000.0003900.0000000.0007800.02563.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
62017Agua Prieta0.0001200.0000000.0001960.0000980.0013490.091929.00.5000000.5000000.6666670.5370370.4814810.3888890.5000000.4444440.5000000.2222220.3148150.2962960.8148150.4074070.166667
72018Agua Prieta0.0001520.0000220.0000540.0001200.0014360.091929.00.6153850.5384620.7307690.5769230.5384620.4615380.4615380.6153850.6730770.2115380.3846150.3846150.6923080.4230770.230769
82019Agua Prieta0.0003700.0000000.0002070.0000870.0032630.091929.00.7647060.3725490.6078430.5882350.5294120.3921570.3529410.4313730.5294120.2549020.3137250.2941180.7450980.4313730.156863
92020Agua Prieta0.0003260.0000220.0001090.0000870.0061240.091929.00.4166670.3500000.6166670.6500000.3833330.4333330.5666670.5500000.5166670.2666670.3333330.3666670.1666670.7166670.483333
AñoMunicipioAbuso sexualFeminicidioHomicidioNarcomenudeoRoboSecuestroPoblacionTiene PreocupacionColonia InseguraMunicipio InseguroEstado InseguroAfectacion Forma de VidaConfianza en TransitoConfianza en Policia MunConfianza en Policia EstConfianza en Guardia NaConfianza en Policia MinConfianza en Ministerio PubConfianza en Fiscalia G RConfianza en EjercitoConfianza en MarinaConfianza en Jueces
4232020Villa Pesqueira0.00.0000000.0019180.0000000.0000000.01043.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
4242021Villa Pesqueira0.00.0000000.0000000.0000000.0019180.01043.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
4252022Villa Pesqueira0.00.0000000.0009590.0000000.0000000.01043.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
4262017Yécora0.00.0000000.0025040.0006260.0018780.04793.00.6923080.6153850.7692310.4615380.6923080.1538460.3846150.5384620.3846150.1538460.1538460.3076920.9230770.6153850.230769
4272018Yécora0.00.0002090.0037550.0002090.0016690.04793.00.7333330.5333330.7333330.4000000.7333330.2000000.4666670.5333330.4666670.1333330.0666670.2000000.8666670.3333330.066667
4282019Yécora0.00.0000000.0006260.0000000.0022950.04793.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
4292020Yécora0.00.0000000.0025040.0000000.0050070.04793.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
4302021Yécora0.00.0000000.0014600.0000000.0106410.04793.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
4312022Yécora0.00.0000000.0002090.0000000.0035470.04793.00.5625000.6875000.8750000.6250000.6875000.1250000.3750000.4375000.5625000.3125000.1875000.3125000.8125000.7500000.187500
4322019MazatanNaNNaNNaNNaNNaNNaNNaN0.6666670.6111110.8333330.6111110.2777780.5000000.4444440.6666670.3888890.5000000.3333330.4444440.7222220.5555560.388889